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Table 6. 
City-Level Outcome of City Upgrading Using Propensity Score Matching
PSM ModelPSM + Fixed EffectsFan, Li, and Zhang (2012)
City-Level Govt. Activities Outcome    
Number of public employees 414.9000** 407.5000** 995*** 
Share of productive expenditure −0.0058** −0.0058** −0.026*** 
Share of agriculture tax −0.0586*** −0.0588*** −0.053*** 
Post-upgrade average GDP growth −0.0007 −0.0008 
Number of firm births 4.3710*** 4.8440*** 
Log tax from business income 0.5150*** 0.5170*** 
Controls Block FE Block FE County FE 
  Year FE Year FE 
PSM ModelPSM + Fixed EffectsFan, Li, and Zhang (2012)
City-Level Govt. Activities Outcome    
Number of public employees 414.9000** 407.5000** 995*** 
Share of productive expenditure −0.0058** −0.0058** −0.026*** 
Share of agriculture tax −0.0586*** −0.0588*** −0.053*** 
Post-upgrade average GDP growth −0.0007 −0.0008 
Number of firm births 4.3710*** 4.8440*** 
Log tax from business income 0.5150*** 0.5170*** 
Controls Block FE Block FE County FE 
  Year FE Year FE 

FE = fixed effects, GDP = gross domestic product, PSM = propensity score matching.

Notes:

1. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively.

2. The PSM model uses variables of the three upgrading requirements and their interactions to generate a propensity score.

Source: Authors’ calculations.

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